The connected world, also referred to as the Internet of Things or IOT, is growing quickly. Analysts have estimated that along with the continued growth of humans using the Internet, the number of connected devices and systems will rise from 5 Billion to 1 Trillion in the next 10 years. However, the traditional ways to manage and communicate with these systems has not changed, meaning that all the information from these systems is not accessible or is not able to be correlated in a way that helps people or businesses do their jobs better and more efficiently, find information they are looking for in the proper context, or make this data consumable in a meaningful way. In addition, user expectations for interacting with systems have changed. Social networks and Mashup web pages have become the common way for users to consume data and interact with other people.
There are a variety of specific solutions to handle the rising amount of data found in industry today. They can be categorized into the following: Enterprise Resource Planning (ERP) systems; Portals and related technologies; Traditional Business Intelligence systems; and Manufacturing Intelligence systems.
Enterprise Resource Planning systems are used by large and small companies to run their businesses. The minimal requirement is to provide financial and accounting services. However, these systems typically have functionality for specific vertical industries, such as manufacturing, utilities, construction, retail, etc. These systems are rigid, in both business process support and data models. They are very expensive to implement and maintain. They are implemented to enforce repeatable, standard business processes. Traditionally it has been impossible to use these systems for dynamic business processes or interactive problem solving.
Portals are a way for companies to share information through a thin client (browser). Usually, a number of documents and data sources are used to publish information for a large user base. The information, while searchable, is relatively static and does not address current conditions or interactive problem solving.
Traditional business intelligence solutions usually rely on specific, detailed data models (often data warehouses). While the data is typically “fresh” (about a day old) in these systems, the models are rigid and report writing may require Information Technology (IT) skills. While these solutions have become much better at providing users with the ability to self-serve, the self service capability is restricted to previously designed semantic models. These solutions do not address current conditions, rapidly changing data, third party collaboration, or external data sources.
Manufacturing Intelligence solutions (also referred to as Enterprise Manufacturing Intelligence or EMI) are concerned with the more real-time data that is collected from machines and devices. This data is usually time series data and does not have business context associated with it. The consumers of these applications are usually plant operators and engineers. These applications do not handle other business related data, do not understand or correlate unstructured data, and are not “document” friendly.
The currently utilized solution to pull all these separate sources of data together, so that users can consume data from more than one of these solutions in a meaningful way, is to execute a complex, multi-year integration project that results in a data mart. This usually involves replicating large quantities of data from multiple systems into a rigid model, similar to a hub and spoke model. The hub is the data mart holding all the replicated data. As the systems change at the end of the spokes, new integration and modeling is required. This type of solution is expensive to maintain. The data model and semantics are not dynamic. And the ability to consume the data is available only through pre-defined reports.
Additionally, the traditional applications that rely on relational data bases are adept at answering known questions against known data structures (Known-Known). Search engines and related applications can answer known questions against unknown data structures (Known-Unknown). The problem at hand is how to handle the above scenarios, but also answer unknown questions against known data structures (Unknown-Known), and unknown questions against unknown data structures (Unknown-Unknown).
Most software applications allow a user or developer to manipulate data within the application. Accordingly, existing technologies have developed design tools to assist application software developer in designing an application interface.
Unfortunately, existing interface development technologies and designs have not kept pace with the increasing demand for interfaces. For example, the existing interface development technologies are not equipped to address current conditions, such as rapidly changing data sets which are accessible in different manners, at different locations and in different formats. Attempts with existing interface development technologies to provide self service capability have been limited to previously designed semantic models. Further, many of these design tools require specialized training to be able to use them to develop an application interface.
To meet these increased demands for interfaces, developers of these interfaces need all the required information for the application interface to be easily and readily available. Additionally, developers of these interfaces need to be able quickly obtain and understand all of the relationships that exist within the application. This technology's unique model-based design and development tools enable developers to build and deploy operational solutions in less time than traditional approaches.